Artificial Intelligence Inspired Multi-Dimensional Traffic Control for Heterogeneous Networks

摘要

The heterogeneous network is the foundation of next-generation networks. It aims to explore the existing network resources effectively, and providing better QoS for every kind of traffic flow as far as possible. However, the diversity and dynamic nature of heterogeneous networks will bring a huge burden and big data to the network traffic control. Therefore, how to achieve efficient and intelligent network traffic control becomes the key problem of heterogeneous networks. In this article, an AI-inspired traffic control scheme is proposed. In order to realize fine-grained traffic control in heterogeneous networks, multi-dimensional (i.e., inter-layer, intra-layer, and caching and pushing) network traffic control is introduced. It is worth noting that backpropagation in deep recurrent neural networks is applied in the intra-layer such that an intelligent traffic control scheme can be derived efficiently when facing the huge traffic load in heterogeneous networks. Moreover, DBSCAN is adopted in the inter-layer, which supports efficient classification in the inter-layer. In addition, caching and pushing is adopted to make full use of network resources and provide better QoS. Simulation results demonstrate the effectiveness and practicability of the proposed scheme.